Generative models for content creation.   After years of development, deep generative models are able to produce high-quality, high-resolution images [1]}, [2]}, [3]}, [4]}, powering a wide range of computer vision and graphics applications. Recent examples include image projection and editing with GANs [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, image-to-image translation [11]}, [12]}, [13]}, [14]}, simulation-to-real [15]}, [16]}, and domain adaptation [17]}, [18]}, [19]}. The advance of generative models comes at the cost of intensive computation [3]}, [21]}, the construction of large-scale high-quality datasets [22]}, [23]}, [2]}, and domain expertise on model training [25]}, [21]}. As a result, recent advanced models are often developed in research labs with abundant computing and human resources. Different from prior works, we aim to help novice users quickly customize their own models without tedious data collection and domain knowledge. We achieve it using a sketching interface and our cross-domain fine-tuning method. Similar to our work, model rewriting [27]} aims to change the rules of a pre-trained model through user interaction. Compared to the object copy-and-paste tool used in model rewriting, our sketching interface provides new capabilities. For example, it is much easier to describe the object shape, pose, and scene layout through quick sketching, compared to finding parts from different images and compositing them together.
